Fuzzy Hoeffding Decision Trees for Learning Analytics

Virtual Learning Environments (VLEs) are online educational platforms that combine static educational content with interactive tools to support the learning process. Click-based data, reporting the students' interactions with the VLE, are continuously collected, so automated methods able to manage big, non-stationary, and changing data are necessary to extract useful knowledge from them. Moreover, automatic methods able to explain their results are needed, especially in sensitive domains such as the educational one, \rp{where} users need to understand and trust the process leading to the results. This paper compares two adaptive and interpretable algorithms (Hoeffding Decision Tree and its fuzzy version) for predicting exam failure/success of students. Experiments, conducted on a subset of the Open University Learning Analytics (OULAD) dataset, demonstrate the reliability of the adaptive models in accurately classifying the evolving educational data and the effectiveness of the fuzzy methods in returning interpretable results.